Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling
This addresses the problem of costly multispectral sensors for imaging applications, offering a more accessible alternative, though it appears incremental as it builds on existing sensor and modeling techniques.
The study tackled the challenge of simultaneous multispectral imaging by developing a framework using conventional color image sensors with numerical demultiplexing via non-linear random forest modeling, resulting in higher resolution reflectance spectra from sensor measurements.
The simultaneous capture of imaging data at multiple wavelengths across the electromagnetic spectrum is highly challenging, requiring complex and costly multispectral image sensors. In this study, we introduce a comprehensive framework for performing simultaneous multispectral imaging using conventional image sensors with color filter arrays via numerical demultiplexing of the color image sensor measurements. A numerical forward model characterizing the formation of sensor measurements from light spectra hitting the sensor is constructed based on a comprehensive spectral characterization of the sensor. A numerical demultiplexer is then learned via non-linear random forest modeling based on the forward model. Given the learned numerical demultiplexer, one can then demultiplex simultaneously-acquired measurements made by the image sensor into reflectance intensities at discrete selectable wavelengths, resulting in a higher resolution reflectance spectrum. Simulation and real-world experimental results demonstrate the efficacy of such a method for simultaneous multispectral imaging.